PCA and Clustering

This tutorial introduces examples, including the analysis of handwritten digits, and then applies PCA to reduce the dimensionality of the data set. Observe how it connects with programming concepts introduced in the previous unit dealing with PCA.

Introduction

The main feature of unsupervised learning algorithms, when compared to classification and regression methods, is that input data are unlabeled (i.e. no labels or classes are given) and that the algorithm learns the structure of the data without any assistance. This creates two main differences. First, it allows us to process large amounts of data because the data does not need to be manually labeled. Second, it is difficult to evaluate the quality of an unsupervised algorithm due to the absence of an explicit goodness metric as used in supervised learning.

One of the most common tasks in unsupervised learning is dimensionality reduction. On one hand, dimensionality reduction may help with data visualization (e.g. t-SNA method) while, on the other hand, it may help deal with the multicollinearity of your data and prepare the data for a supervised learning method (e.g. decision trees).

Source: Sergey Korolev, https://www.kaggle.com/code/kashnitsky/topic-7-unsupervised-learning-pca-and-clustering/notebook
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